Chemical Structure-Based Model for Estimation of the Upper Flammability Limit of Pure Compounds

In the present work, a new molecular-based model is presented for estimation of the upper flammability limit (UFL) of pure compounds. The parameters of the model are the number of occurrences of a new collection of 113 functional groups. On the basis of these 113 functional groups, a feed-forward neural network is presented to estimate the UFL of pure compounds. The squared correlation coefficient, absolute percent error, standard deviation, and root-mean-square error of the model over the 867 pure compounds used for the development of the model are 0.9469, 7.07%, 0.883, 0.882, respectively. Therefore, the model is accurate and can be used to predict the UFL for a wide range of pure compounds.